Share via

Microsoft.MachineLearningServices workspaces/data 2022-10-01

  • Article

Bicep resource definition

The workspaces/data resource type can be deployed with operations that target:

For a list of changed properties in each API version, see change log.

Resource format

To create a Microsoft.MachineLearningServices/workspaces/data resource, add the following Bicep to your template.

resource symbolicname 'Microsoft.MachineLearningServices/workspaces/data@2022-10-01' = {
  name: 'string'
  properties: {
    dataType: 'string'
    description: 'string'
    isArchived: bool
    properties: {
      {customized property}: 'string'
    }
    tags: {
      {customized property}: 'string'
    }
  }
}

Property values

DataContainerProperties

Name Description Value
dataType [Required] Specifies the type of data. 'mltable'
'uri_file'
'uri_folder' (required)
description The asset description text. string
isArchived Is the asset archived? bool
properties The asset property dictionary. ResourceBaseProperties
tags Tag dictionary. Tags can be added, removed, and updated. ResourceBaseTags

Microsoft.MachineLearningServices/workspaces/data

Name Description Value
name The resource name string

Constraints:
Pattern = ^[a-zA-Z0-9][a-zA-Z0-9\-_]{0,254}$ (required)
parent In Bicep, you can specify the parent resource for a child resource. You only need to add this property when the child resource is declared outside of the parent resource.

For more information, see Child resource outside parent resource.		 Symbolic name for resource of type: workspaces
properties [Required] Additional attributes of the entity. DataContainerProperties (required)

ResourceBaseProperties

Name Description Value

ResourceBaseTags

Name Description Value

Quickstart samples

The following quickstart samples deploy this resource type.

Bicep File Description
Create a Data Asset from File URI This template creates a data asset/container from file URI in an Azure Machine Learning workspace.

ARM template resource definition

The workspaces/data resource type can be deployed with operations that target:

For a list of changed properties in each API version, see change log.

Resource format

To create a Microsoft.MachineLearningServices/workspaces/data resource, add the following JSON to your template.

{
  "type": "Microsoft.MachineLearningServices/workspaces/data",
  "apiVersion": "2022-10-01",
  "name": "string",
  "properties": {
    "dataType": "string",
    "description": "string",
    "isArchived": "bool",
    "properties": {
      "{customized property}": "string"
    },
    "tags": {
      "{customized property}": "string"
    }
  }
}

Property values

DataContainerProperties

Name Description Value
dataType [Required] Specifies the type of data. 'mltable'
'uri_file'
'uri_folder' (required)
description The asset description text. string
isArchived Is the asset archived? bool
properties The asset property dictionary. ResourceBaseProperties
tags Tag dictionary. Tags can be added, removed, and updated. ResourceBaseTags

Microsoft.MachineLearningServices/workspaces/data

Name Description Value
apiVersion The api version '2022-10-01'
name The resource name string

Constraints:
Pattern = ^[a-zA-Z0-9][a-zA-Z0-9\-_]{0,254}$ (required)
properties [Required] Additional attributes of the entity. DataContainerProperties (required)
type The resource type 'Microsoft.MachineLearningServices/workspaces/data'

ResourceBaseProperties

Name Description Value

ResourceBaseTags

Name Description Value

Quickstart templates

The following quickstart templates deploy this resource type.

Template Description
Create a Data Asset from File URI

Deploy to Azure		 This template creates a data asset/container from file URI in an Azure Machine Learning workspace.

Terraform (AzAPI provider) resource definition

The workspaces/data resource type can be deployed with operations that target:

  • Resource groups

For a list of changed properties in each API version, see change log.

Resource format

To create a Microsoft.MachineLearningServices/workspaces/data resource, add the following Terraform to your template.

resource "azapi_resource" "symbolicname" {
  type = "Microsoft.MachineLearningServices/workspaces/data@2022-10-01"
  name = "string"
  body = jsonencode({
    properties = {
      dataType = "string"
      description = "string"
      isArchived = bool
      properties = {
        {customized property} = "string"
      }
      tags = {
        {customized property} = "string"
      }
    }
  })
}

Property values

DataContainerProperties

Name Description Value
dataType [Required] Specifies the type of data. 'mltable'
'uri_file'
'uri_folder' (required)
description The asset description text. string
isArchived Is the asset archived? bool
properties The asset property dictionary. ResourceBaseProperties
tags Tag dictionary. Tags can be added, removed, and updated. ResourceBaseTags

Microsoft.MachineLearningServices/workspaces/data

Name Description Value
name The resource name string

Constraints:
Pattern = ^[a-zA-Z0-9][a-zA-Z0-9\-_]{0,254}$ (required)
parent_id The ID of the resource that is the parent for this resource. ID for resource of type: workspaces
properties [Required] Additional attributes of the entity. DataContainerProperties (required)
type The resource type "Microsoft.MachineLearningServices/workspaces/data@2022-10-01"

ResourceBaseProperties

Name Description Value

ResourceBaseTags

Name Description Value